An improved teaching-learning-based optimization (TLBO) algorithm based on hybrid learning strategies and disturbance was proposed to improve the searching functions of the algorithm and solve the problem of being easy to fall into local optima. The mutation strategy of differential evolution algorithm was merged into the learning part of the algorithm, and a hybrid learning strategy was propased to improve the learning ability of students in the later learning as well as the convergence performance of the algorithm. A new disturbance strategy was constructed in the late stage to reduce the possibility of trapping into local optima and ensure global optimality. Experimental results based on the standard test function demonstrate that the proposed algorithm can effectively increase the convergence speed and accuracy and significantly advance the optimization compared with the current similar four kinds of algorithms with excellent performance.
BI Xiao-jun, WANG Jia-hui. Teaching-learning-based optimization algorithm with hybrid learning strategy. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(5): 1024-1031.
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